Robotic surgical system with artificial intelligence

Abstract

A surgical robot is coupled to the surgeon console. The surgical robot performs a robotic surgical procedure. The surgical robot includes one or more robotic surgical arms. A control system is coupled to the one or more robotic surgical arms. An artificial intelligence (“AI”) system includes a plurality of machine learning algorithms. The robotic surgical arms are at least partially controlled by the AI system and the control device to process intraoperative data including images captured by cameras and sensor inputs. The machine learning algorithms analyze the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases. The one or more machine algorithms enable at least partial identification of anatomical structures. In response to detection of the anatomical structures the AI system at least partially adjusts movement of the robotic surgical arms to avoid critical anatomical structures while performing the robotic surgery procedure to ensure precise targeting at the surgical site while minimizing damage to surrounding tissue at a surgical site. The AI system provides a surgeon with improved dexterity when the surgeon uses the robotic surgical arms at the surgical site, the improved dexterity resulting from at least partially analyzing the intraoperative data in real time by the one or more machine learning algorithms, enabling precise and adaptive manipulation of the robotic surgical arms at the surgical site.

1 . A surgical system, comprising:
 a surgeon console including a display, and a planning module that allows a surgeon to create a plan for a robotic surgery procedure, the surgeon console being coupled to a robotic surgical system that includes an image recognition database and a procedure database, a surgical computing device coupled to the robotic surgery control system, the surgical computing device including a memory with stored surgical instructions;   a surgical robot coupled to the surgeon console, the surgical robot configured to perform a robotic surgical procedure, the surgical robot including one or more robotic surgical arms;   a control system coupled to the one or more robotic surgical arms;   an artificial intelligence (“AT”) system with a plurality of machine learning algorithms, the robotic surgical arms at least partially controlled by the AI system nd the control device to process intraoperative data including images captured by cameras and sensor inputs, the machine learning algorithms analyzing the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases, the one or more machine algorithms enabling at least partial identification of anatomical structures, in response to detection of the anatomical structures the AI system at least partially adjusts movement of the robotic surgical arms to avoid critical anatomical structures while performing the robotic surgery procedure to ensure precise targeting at the surgical site while minimizing damage to surrounding tissue at a surgical site; the AI system providing a surgeon with improved dexterity when the surgeon uses the robotic surgical arms at the surgical site, the improved dexterity resulting from at least partially analyzing the intraoperative data in real time by the one or more machine learning algorithms enabling precise and adaptive manipulation of the robotic surgical arms at the surgical site;   one or more computer processors used in the robotic surgery procedure;   a non-transitory computer-readable storage medium storing computer instructions, which when executed by the one or more computer processors, cause the surgical system to monitor the robotic surgery procedure;   executing a planned surgical step of the robotic surgery procedure using the one or more machine learning algorithms; and   determining that the robotic surgery procedure has been completed.   
     
     
         2 . The system of  claim 1 , wherein the anatomical structure are selected from one or more of: tumors; blood vessels, and; nerves. 
     
     
         3 . The system of  claim 1 , wherein the anatomical structures are selected from one or more of: skin, subcutaneous tissue, adipose tissue, fascia, muscle, tendons, ligaments, bones, joints, cartilage, hollow or solid organs, vascular structures (arteries, veins, capillaries, lymphatic vessels and nodes), peripheral nerves, spinal cord and nerve roots, autonomic nerves, peritoneum, pleura, pericardium, and tumors. 
     
     
         4 . The system of  claim 1 , wherein dentification of the anatomical structures intra-operatively is facilitated by anatomical landmarks. 
     
     
         5 . The system of  claim 1 , wherein once the anatomical structures are identified, AI system calculates optimal robotic system arm movements. 
     
     
         6 . The system of  claim 1 , AI system calculates optimal robotic system arm movements and adjusts parameters, including one or more of: force, and angle to ensure precise targeting. 
     
     
         7 . The system of  claim 1 , wherein the system continuously monitors the interaction between the robotic surgical arms and the surgical site. 
     
     
         8 . The system of  claim 1 , wherein system leverages predictive modeling and historical data to refine movement predictions. 
     
     
         9 . The system of  claim 1 , wherein patterns learned from prior surgical procedures are used for the surgical procedure. 
     
     
         10 . The system of  claim 1 , wherein the machine learning algorithms assign confidence scores to each planned movement based on the analysis of intraoperative data. 
     
     
         11 . The system of  claim 1 , wherein the machine learning algorithms assign confidence scores to each planned movement based on the analysis of intraoperative data to guide the surgeon to an a selected movement path of the surgical robot arms. 
     
     
         12 . The system of  claim 1 , wherein enhanced dexterity also includes the ability to make one or more of: ultra-fine movements, and precise dissections, by utilizing feedback. 
     
     
         13 . The system of  claim 1 , wherein during tumor resection the system can detect and adjust for differences in tissue texture. 
     
     
         14 . At least a portion of the intraoperative data including noisy data, including errors, outliers, and inconsistencies. 
     
     
         15 . The system of  claim 14 , wherein the system provides functionality for identifying, cleaning, and transforming the noisy data to optimize its use in machine learning algorithms. 
     
     
         16 . A surgical system, comprising:
 a surgeon console including a display, and a planning module that allows a surgeon to create a plan for a robotic surgery procedure, the surgeon console being coupled to a robotic surgical system that includes an image recognition database and a procedure database, a surgical computing device coupled to the robotic surgery control system, the surgical computing device including a memory with stored surgical instructions;   a surgical robot coupled to the surgeon console, the surgical robot configured to perform a robotic surgical procedure, the surgical robot including one or more robotic surgical arms;   an artificial intelligence (“AI”) system with a plurality of machine learning algorithms, the robotic surgical arms at least partially controlled by the AI system to process intraoperative data including images captured by cameras and sensor inputs, the machine learning algorithms analyzing the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases, the one or more machine algorithms enabling at least partial identification of anatomical structures, in response to detection of the anatomical structures the AI system at least partially adjusts movement of the robotic surgical arms to avoid critical anatomical structures while performing the robotic surgery procedure to ensure precise targeting at the surgical site while minimizing damage to surrounding tissue at a surgical site; the AI system providing a surgeon with improved dexterity when the surgeon uses the robotic surgical arms at the surgical site, the improved dexterity resulting from at least partially analyzing the intraoperative data in real time by the one or more machine learning algorithms enabling precise and adaptive manipulation of the robotic surgical arms at the surgical site;   one or more computer processors used in the robotic surgery procedure;   a non-transitory computer-readable storage medium storing computer instructions, which when executed by the one or more computer processors, cause the surgical system to monitor the robotic surgery procedure;   executing a planned surgical step of the robotic surgery procedure using the one or more machine learning algorithms; and   determining that the robotic surgery procedure has been completed.   
     
     
         17 . The system of  claim 1 , wherein the anatomical structure are selected from one or more of: tumors; blood vessels, and; nerves. 
     
     
         18 . The system of  claim 1 , wherein the anatomical structures are selected from one or more of: skin, subcutaneous tissue, adipose tissue, fascia, muscle, tendons, ligaments, bones, joints, cartilage, hollow or solid organs, vascular structures (arteries, veins, capillaries, lymphatic vessels and nodes), peripheral nerves, spinal cord and nerve roots, autonomic nerves, peritoneum, pleura, pericardium, and tumors. 
     
     
         19 . The system of  claim 1 , wherein dentification of the anatomical structures intra-operatively is facilitated by anatomical landmarks. 
     
     
         20 . The system of  claim 1 , wherein once the anatomical structures are identified, AI system calculates optimal robotic system arm movements.